Embedded wireless networks have been explored for closed-loop, real-time sensing and control. In industrial automation, for instance, wireless-enabled mobile, pervasive, and reconfigurable instrumentation and the significant cost of planning, installing, and maintaining wired network cables have made wireless networks attractive for industrial monitoring and control. Industrial wireless networking standards such as WirelessHART, ISA100.11a, and WIA-PA have been defined and deployed in practice. In road transportation, wireless communication has become a basic enabler for connected and automated vehicles, which cooperate with one another and with transportation infrastructures to ensure safety, maximize fuel economy, and minimize emission as well as congestion. Machine-type communication for real-time sensing and control has also become a major focus of emerging 5G wireless network research and development. Wireless communication is also a basic enabler for networked augmented reality, for instance, by integrating spatially distributed cameras into collaborative augmented vision systems.
In wireless networked sensing and control (WSC), communication across wireless networks is a basic enabler for coordination among distributed sensors, controllers, and actuators. The mission-critical nature of real-time WSC tasks, such as the control of industrial plants and vehicles and/or the seamless experience of augmented reality, requires predictable reliability, timeliness, and throughput in wireless communication. Nonetheless, wireless communication is subject to inherent dynamics and uncertainties within the system and environment. Wireless communication channels exhibit complex, environment-specific spatiotemporal dynamics and uncertainties. Interference between concurrent transmissions is a major source of uncertainty, and the interference cuts across multiple aspects of wireless networking such as scheduling, channel assignment, power control, rate control, and routing. Dynamic control strategies in WSC systems introduce dynamic network traffic patterns and pose different requirements on communication reliability, timeliness, and throughput; in vehicular WSC systems, vehicle mobility introduces another dimension of uncertainty and complexity.
While wireless networking has been extensively studied, existing mechanisms have not solved fundamental problems such as predictable interference control in the presence of uncertainties and predictable multi-hop real-time communication in the presence of fast-varying, probabilistic path delays. Hence we still lack mechanisms for ensuring predictable communication reliability, timeliness, and throughput. Accordingly, current research and practice adopts a centralized architecture where a network manager centrally collects statistics of the network state (e.g., wireless channel gain) and centrally decides routing paths and transmission schedules. The centralized architecture makes it difficult to ensure predictable communication reliability and timeliness in the presence of uncertainties.
Without addressing predictable control of interference among concurrent transmitters, these works have largely avoided channel spatial reuse too, leading to underutilization of network real-time capacity. The deficiencies of centralized architectures and no-channel-spatial-reuse are especially acute in large-scale WSC networks. Having not resolved fundamental challenges posed by interference and probabilistic path delays, existing distributed approaches in industrial and vehicular WSC systems cannot ensure predictable reliability and timeliness in communication.
Accordingly, the current real-world deployments of WSC systems have mostly been limited to open-loop sensing such as industrial monitoring and vehicle active-safety warning.